generate audio fix

api.py

@@ -53,42 +53,25 @@ def get_comparative_analysis(company: str):
 
 
 
-
+# Generate audio summary
 @app.get("/generate-audio/")
 def generate_audio(company: str):
-    """Generates an LLM-based summary and provides a Hindi audio output."""
+    """Generates a Hindi audio summary using LLM response."""
     
-    # ✅ Step 1: Extract news articles
+    # ✅ Extract 10 news articles
     articles = extract_news(company)[:10]
     if not articles:
         raise HTTPException(status_code=404, detail="No articles found for the given company.")
 
-    # ✅ Step 2: Generate the LLM-based summary
+    # ✅ Generate LLM-based sentiment summary
     summary_text = summarize_overall_sentiment(articles)
 
-    # ✅ Step 3: Convert summary to Hindi audio (returns a buffer)
+    # ✅ Convert summary to Hindi speech
     audio_buffer = generate_hindi_speech(summary_text)
 
-    # ✅ Step 4: Store the audio file in memory
-    audio_filename = "hindi_summary.mp3"
-    audio_buffer.seek(0)
-
-    # ✅ Step 5: Return JSON response with summary and audio file link
-    return JSONResponse(
-        content={
-            "summary": summary_text,
-            "audio_url": f"/download-audio/?filename={audio_filename}"
-        }
-    )
-
-@app.get("/download-audio/")
-def download_audio(filename: str):
-    """Streams the generated audio file."""
-    audio_buffer = generate_hindi_speech("Placeholder text")  # Replace with actual generated buffer
-    audio_buffer.seek(0)
-
+    # ✅ Return only the Hindi audio as a file response
     return StreamingResponse(audio_buffer, media_type="audio/mpeg", headers={
-        "Content-Disposition": f"attachment; filename={filename}"
+        "Content-Disposition": "attachment; filename=hindi_summary.mp3"
     })
 
 

app.py

@@ -89,7 +89,7 @@ if compare_news:
                 st.write(f"**Unique Topics in Article {article_number}:** {', '.join(unique_topics) if unique_topics else 'None'}")
 
         # ✅ Display Final LLM-Based Sentiment Analysis
-        st.write("### LLM-Based Sentiment Summary")
+        st.write("## Overall Sentiment Summary")
         final_llm_summary = comparison_data.get("Final Sentiment Analysis", "No summary available.")
         st.info(f"**{final_llm_summary}**")
 
@@ -100,24 +100,14 @@ if compare_news:
 
 # Generate Hindi Speech Audio
 if generate_audio:
-    st.write("## Overall Summary")
-
-    response = requests.get(f"{FASTAPI_URL}/generate-audio/", params={"company": company})
-
-    if response.status_code == 200:
-        data = response.json()
-        summary_text = data.get("summary", "No summary available.")
-        audio_url = data.get("audio_url", "")
-
-        # ✅ Display the text summary
-        st.write("### Text Summary")
-        st.info(f"**{summary_text}**")
-
-        # ✅ Play the audio output (Correct URL)
-        if audio_url:
-            st.audio(f"{FASTAPI_URL}{audio_url}")  # Use the correct audio download link
-        else:
-            st.warning("Audio file is not available.")
-
-    else:
-        st.error(f"Error generating audio: {response.status_code}")
+    st.write("### Hindi Audio Summary")
+    
+    # ✅ Directly play the audio from API
+    st.audio(f"{FASTAPI_URL}/generate-audio/?company={company}", format="audio/mp3")
+
+    # ✅ Download button for Hindi summary
+    audio_data = requests.get(f"{FASTAPI_URL}/generate-audio/?company={company}").content
+    st.download_button(label="Download Hindi Audio",
+                       data=audio_data,
+                       file_name="hindi_summary.mp3",
+                       mime="audio/mpeg")

research/trials.ipynb

@@ -388,34 +388,6 @@
     "from src.comparison import comparative_analysis"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "BadRequestError",
-     "evalue": "Error code: 400 - {'error': {'message': 'Organization has been restricted. Please reach out to support if you believe this was in error.', 'type': 'invalid_request_error', 'code': 'organization_restricted'}}",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[34], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m comparison_data \u001b[38;5;241m=\u001b[39m \u001b[43mcomparative_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43marticles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m comparison_data\n",
-      "File \u001b[1;32mc:\\Projects\\News-Summarization-and-Text-to-Speech-Application\\src\\comparison.py:41\u001b[0m, in \u001b[0;36mcomparative_analysis\u001b[1;34m(articles)\u001b[0m\n\u001b[0;32m     38\u001b[0m sentiment_summary \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOverall sentiment is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moverall_sentiment\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformatted_counts\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNegative\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;250m \u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Negative, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformatted_counts\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPositive\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;250m \u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Positive).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m# ✅ LLM-Based Sentiment Summary\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m overall_summary \u001b[38;5;241m=\u001b[39m \u001b[43msummarize_overall_sentiment\u001b[49m\u001b[43m(\u001b[49m\u001b[43marticles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;66;03m# ✅ Return the final comparative analysis\u001b[39;00m\n\u001b[0;32m     44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m     45\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSentiment Analysis\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[0;32m     46\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSentiment Distribution\u001b[39m\u001b[38;5;124m\"\u001b[39m: formatted_counts,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFinal Sentiment Analysis\u001b[39m\u001b[38;5;124m\"\u001b[39m: overall_summary  \u001b[38;5;66;03m# ✅ LLM-generated summary\u001b[39;00m\n\u001b[0;32m     54\u001b[0m }\n",
-      "File \u001b[1;32mc:\\Projects\\News-Summarization-and-Text-to-Speech-Application\\src\\summarization.py:26\u001b[0m, in \u001b[0;36msummarize_overall_sentiment\u001b[1;34m(articles)\u001b[0m\n\u001b[0;32m     12\u001b[0m concatenated_text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(article[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msummary\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m article \u001b[38;5;129;01min\u001b[39;00m articles)\n\u001b[0;32m     14\u001b[0m prompt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;124mYou are an AI language model designed for news sentiment summarization.\u001b[39m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;124mAnalyze the following news articles and provide an **overall sentiment summary** (Positive, Negative, or Neutral) \u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;124m- **Explanation:** [Brief summary of why this sentiment was chosen]\u001b[39m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;124m\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m---> 26\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmistral-saba-24b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# ✅ Use a powerful model from Groq\u001b[39;49;00m\n\u001b[0;32m     28\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrole\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msystem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mYou are a sentiment analysis assistant.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[43m              \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrole\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m}\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     30\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m250\u001b[39;49m\n\u001b[0;32m     31\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mcontent\u001b[38;5;241m.\u001b[39mstrip()\n",
-      "File \u001b[1;32mc:\\Projects\\News-Summarization-and-Text-to-Speech-Application\\tts\\lib\\site-packages\\groq\\resources\\chat\\completions.py:322\u001b[0m, in \u001b[0;36mCompletions.create\u001b[1;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, n, parallel_tool_calls, presence_penalty, reasoning_format, response_format, seed, service_tier, stop, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m    166\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[0;32m    167\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    168\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    198\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m 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\u001b[49m\u001b[43mreasoning_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    339\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse_format\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    340\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseed\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    341\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mservice_tier\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    342\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    343\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    344\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtemperature\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    345\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtool_choice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m            \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    356\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    357\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    358\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    359\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Projects\\News-Summarization-and-Text-to-Speech-Application\\tts\\lib\\site-packages\\groq\\_base_client.py:1225\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[1;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[0;32m   1211\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[0;32m   1212\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1213\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1220\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1221\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m   1222\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[0;32m   1223\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[0;32m   1224\u001b[0m     )\n\u001b[1;32m-> 1225\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
-      "File \u001b[1;32mc:\\Projects\\News-Summarization-and-Text-to-Speech-Application\\tts\\lib\\site-packages\\groq\\_base_client.py:917\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    915\u001b[0m     retries_taken \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m--> 917\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    918\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    919\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    920\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    921\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    922\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    923\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Projects\\News-Summarization-and-Text-to-Speech-Application\\tts\\lib\\site-packages\\groq\\_base_client.py:1020\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[0;32m   1017\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m   1019\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1020\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1022\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[0;32m   1023\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m   1024\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1028\u001b[0m     retries_taken\u001b[38;5;241m=\u001b[39mretries_taken,\n\u001b[0;32m   1029\u001b[0m )\n",
-      "\u001b[1;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': 'Organization has been restricted. Please reach out to support if you believe this was in error.', 'type': 'invalid_request_error', 'code': 'organization_restricted'}}"
-     ]
-    }
-   ],
-   "source": [
-    "comparison_data = comparative_analysis(articles)\n",
-    "comparison_data"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

src/summarization.py

@@ -26,13 +26,12 @@ def summarize_overall_sentiment(articles):
     {concatenated_text}
 
     Your response should be structured as follows:
-    - Sentiment: [Positive/Negative/Neutral]
-    - Explanation: [Brief reason why this sentiment was chosen]
+    - [Brief reason why this sentiment was chosen]
     """
 
     # ✅ Use a valid Groq model (Mixtral or LLaMA-3)
     response = client.chat.completions.create(
-        model="mistral-saba-24b",  # ✅ Use "mixtral-8x7b" (Recommended) or "llama3-70b"
+        model="mistral-saba-24b",
         messages=[
             {"role": "system", "content": "You are a sentiment analysis assistant."},
             {"role": "user", "content": prompt}