init

app.py

@@ -1,63 +1,40 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+from gemini_api import get_travel_info, get_travel_recommendations
+from pdf_convert import markdown_to_pdf_weasyprint
+
+def generate_response(input_text):
+    user_query = input_text.strip()
+    if not user_query:
+        print("Please enter a valid travel query!")
+        return
+
+    travel_info = get_travel_info(user_query)
+
+    if travel_info and travel_info.get("location"):
+        travel_recommendations = get_travel_recommendations(
+            travel_info.get("intent"),
+            travel_info.get("location"),
+            travel_info.get("departure_date"),
+            travel_info.get("duration"),
+            travel_info.get("budget"),
+            travel_info.get("num_people"),
+        )
+        pdf = markdown_to_pdf_weasyprint(travel_recommendations)
+    else:
+        print("Could not extract travel information. Please refine your query.")
+
+    return pdf
+
+with gr.Blocks() as demo:
+    gr.Markdown("## PDF Processing with Gemini API")
+    message = "I want to go to Taipei with 6 people, we have three thousand USD for 5 days. we plan we depart at February 2nd"
+    input_text = gr.Textbox(label="User Request", value=message, placeholder="Enter your travel detail here")
+    output_pdf = gr.File(label="Download Output PDF")
+    submit = gr.Button("Generate")
+
+    submit.click(fn=generate_response, inputs=input_text, outputs=output_pdf)
 
 
 if __name__ == "__main__":

gemini_api.py

@@ -0,0 +1,168 @@
+import google.generativeai as genai
+import re
+import json
+import os
+
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
+genai.configure(api_key=GEMINI_API_KEY)
+
+# model = genai.GenerativeModel('gemini-1.5-pro')  # Or 'gemini-pro'
+model = genai.GenerativeModel('gemini-2.0-flash')
+
+def get_travel_info(user_input):
+    """Uses Gemini AI to extract travel intent and location from user input."""
+
+    prompt = f"""
+      You are a travel assistant. Your job is to extract the travel intent, location, and departure date from a user's query.
+      The intent can be anything from planning a trip to finding a hotel to suggesting a restaurant.
+      The location is the destination of the travel.
+      If the location is not specified, return None.
+      If the intent is not related to travel, return the intent and location in JSON format ONLY, like this:
+      If either the intent, the location, the departure_date, duration, budget, or num_people is not found, return null for that field.
+
+      ```json
+      {{
+        "intent": "the extracted intent",
+        "location": "the extracted location",
+        "departure_date": "the extracted departure date",
+        "duration": "the extracted duration",
+        "budget": "the extracted budget",
+        "num_people": "the extracted number of people"
+      }}
+      ```
+
+      User Query: I want to plan a trip to Paris for 5 days with a budget of $2000 for 2 people, leaving on March 15th.
+      ```json
+      {{
+        "intent": "plan a trip",
+        "location": "Paris",
+        "departure_date": "March 15th",
+        "duration": "5 days",
+        "budget": "$2000",
+        "num_people": "2"
+      }}
+      ```
+
+      User Query: Find a hotel in New York City for 3 nights with a budget of $1500 for 1 person, leaving on April 10th.
+      ```json
+      {{
+        "intent": "Find a hotel",
+        "location": "New York City",
+        "departure_date": "April 10th",
+        "duration": "3 nights",
+        "budget": "$1500",
+        "num_people": "1"
+      }}
+      ```
+
+      User Query: What is the weather like today?
+      ```json
+      {{
+        "intent": null,
+        "location": null,
+        "departure_date": null,
+        "duration": null,
+        "budget": null,
+        "num_people": null
+      }}
+    ```
+
+    User Query: {user_input}
+    """
+
+    response = model.generate_content(prompt)
+    response_text = response.text
+    # print(f"Raw Gemini Response: {response_text}")  # Debugging
+
+    try:
+        # Use regular expression to find the JSON string within the response
+        match = re.search(r"\{.*?\}", response_text, re.DOTALL)
+        if match:
+            json_string = match.group(0).strip()
+            data = json.loads(json_string)
+            intent = data.get("intent")
+            location = data.get("location")
+            departure_date = data.get("departure_date")
+            duration = data.get("duration")
+            budget = data.get("budget")
+            num_people = data.get("num_people")
+            return {
+                "intent": intent,
+                "location": location,
+                "departure_date": departure_date,
+                "duration": duration,
+                "budget": budget,
+                "num_people": num_people
+            }
+        else:
+            print("Error: No JSON found in Gemini response.")
+            return None
+
+    except json.JSONDecodeError:
+        print("Error: Could not decode JSON after extraction.")
+        return None
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        return None
+
+def get_travel_recommendations(intent, location, departure_date, duration, budget, num_people):
+    """Uses Gemini AI to provide travel recommendations based on intent and location."""
+
+    prompt = f"""
+      You are a travel assistant. Based on the given intent, location, duration, budget, and number of people, provide helpful travel recommendations in Markdown format.
+      Attractions should provide at least 10 recommendations.
+      Accommodations should provide at least 5 recommendations.
+      Activities should provide at least 10 recommendations.
+      Food should provide at least 10 recommendations.
+      Keep in mind the budget and the number of people when suggesting accommodations and activities.
+
+      Intent: {intent}
+      Location: {location}
+      Departure Date: {departure_date}
+      Duration: {duration}
+      Budget: {budget}
+      Number of people: {num_people}
+
+      Your response should look like this:
+
+      # 🌍 Travel Guide: {location}
+
+      ## 🌤️ Weather:
+      - Weather 1
+
+      ## ✈️ Flight:
+      - Flight 1
+      - Flight 2
+
+      ## 🏨 Accommodations:
+      - Hotel 1
+      - Hotel 2
+
+      ## 💳 Mobile Payment:
+      - Mobile Payment 1
+      - Mobile Payment 2
+
+      ## 🚗 Local Transportations:
+      - Local Transportations 1
+      - Local Transportations 2
+
+      ## 🏰 Attractions:
+      - Attraction 1
+      - Attraction 2
+
+      ## 🚀 Activities:
+      - Activity 1
+      - Activity 2
+
+      ## 🍽️ Foods:
+      - Foods 1
+      - Foods 2
+
+      ## 💡 Tips for Planning:
+      - Tips for Planning 1
+      - Tips for Planning 2
+    """
+
+    response = model.generate_content(prompt)
+    return response.text

pdf_convert.py

@@ -0,0 +1,17 @@
+import markdown
+from weasyprint import HTML, CSS
+
+def markdown_to_pdf_weasyprint(md_content, output_pdf="travel_guide.pdf"):
+    html_content = markdown.markdown(md_content)
+    css = CSS(string="""
+        @font-face {
+            font-family: 'Noto Color Emoji';
+            src: local('Noto Color Emoji'), url(https://github.com/googlefonts/noto-emoji/blob/main/fonts/NotoColorEmoji.ttf?raw=true) format('truetype');
+        }
+        body {
+            font-family: 'Noto Color Emoji', sans-serif;
+            font-size: 14px;
+        }
+    """)
+    HTML(string=html_content).write_pdf(output_pdf, stylesheets=[css])
+    return output_pdf