Create main.py

main.py

@@ -0,0 +1,97 @@
+from fastapi import FastAPI, HTTPException 
+from fastapi.responses import RedirectResponse
+import replicate
+import os
+import json
+from huggingface_hub import InferenceClient
+
+app = FastAPI()
+#defining hf api key
+
+hf_token=os.environ.get("ht_token")
+
+file_path = "replicate_workflow.json"
+
+def load_workflow_from_file(file_path):
+    with open(file_path, 'r') as file:
+        return json.load(file)
+
+
+@app.post("/prompt")
+async def generate_story(prompt: dict):
+    print (prompt)
+    prompt = json.dumps(prompt)
+    client = InferenceClient(model="mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_token)
+    response = client.text_generation(f"""<s><INST>You are a kids story writer. Your task is to analyse the input and provide a nice little short kids story. Given the prompt {prompt}, include "anime style, ultra HD, 4K" in every imageDescription prompt suitable for stable diffusion with upto 20 words, write 6 scenes, maximum of 10 words in comic scene of storyForTheComic, generate a JSON response like [/INST]</s>{[
+ {
+    "sceneNumber": 1,
+    "imageDescription": "Describe the scene in 30 words ",
+    "storyForTheComic": "Write the story here in 10 to 20 words"
+ },
+ {
+    "sceneNumber": 2,
+    "imageDescription": "Describe the scene in 30 words",
+    "storyForTheComic": "Write the story here in 10 to 20 words"
+ },
+ {
+    "sceneNumber": 3,
+    "imageDescription": "Describe the scene in 30 words",
+    "storyForTheComic": "Write the story here in 10 to 20 words"
+ }
+]}""", max_new_tokens=800)
+    print (response)
+    return response
+
+
+@app.post("/workflow")
+async def run_workflow(input: dict):
+    workflow = load_workflow_from_file(file_path)
+    #print (input)    
+    print (input['input'][1])
+
+    workflow["16"]["inputs"]["header_text"] = "Kids going to park" #Title
+    workflow["16"]["inputs"]["footer_text"] = "" #Author
+    workflow["13"]["inputs"]["text_bottom"] = f"{input['input'][7]}" #comic1
+    workflow["83"]["inputs"]["text_bottom"] = f"{input['input'][8]}" #comic2
+    workflow["93"]["inputs"]["text_bottom"] = f"{input['input'][9]}"#comic3
+    workflow["103"]["inputs"]["text_bottom"] =  f"{input['input'][10]}" #comic4
+    workflow["113"]["inputs"]["text_bottom"] = f"{input['input'][11]}" #comic5
+    workflow["123"]["inputs"]["text_bottom"] = f"{input['input'][12]}" #comic6
+    workflow["72"]["inputs"]["text"] = f"{input['input'][1]}"#image1
+    workflow["89"]["inputs"]["text"] = f"{input['input'][2]}"#image2
+    workflow["99"]["inputs"]["text"] = f"{input['input'][3]}"#image3
+    workflow["109"]["inputs"]["text"] = f"{input['input'][4]}"#image4
+    workflow["119"]["inputs"]["text"] = f"{input['input'][5]}"#image5
+    workflow["129"]["inputs"]["text"] = f"{input['input'][6]}"#image6
+
+    auth_token = os.environ.get("REPLICATE_API_TOKEN")
+    if not auth_token:
+        raise HTTPException(status_code=400, detail="REPLICATE_API_TOKEN not found in environment variables")
+
+    output = replicate.run(
+        "fofr/any-comfyui-workflow:5c922132f43c8f7f35825d0687a4733ab974749d7704e6cf0fb62b4a258fa55a",
+        input={
+            "workflow_json": json.dumps(workflow),
+            "randomise_seeds": True,
+            "return_temp_files": False
+        },
+        #auth_token=auth_token
+    )
+    # Extracting the link from the output
+    link = output[0] if isinstance(output, list) and len(output) > 0 else None
+
+    # Returning the link in the response JSON
+    
+    print ({"output": output})
+    print ({"link": link})
+    #image_link = output.get("output",[None])[0]
+    return {"link": link}
+    #return {"output": output}
+    
+    
+
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)