Create app.py

app.py

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+import os
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+from huggingface_hub import InferenceClient
+import uvicorn
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Serve static files for assets
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+# Initialize Hugging Face Inference Client
+client = InferenceClient()
+
+# Pydantic model for API input
+class InfographicRequest(BaseModel):
+    description: str
+
+# Load prompt template from environment variable
+PROMPT_TEMPLATE = os.getenv("PROMPT_TEMPLATE")
+
+# Route to serve the HTML template
+@app.get("/", response_class=HTMLResponse)
+async def serve_frontend():
+    with open("infographic_gen.html", "r") as file:
+        return HTMLResponse(content=file.read())
+
+# Route to handle infographic generation
+@app.post("/generate")
+async def generate_infographic(request: InfographicRequest):
+    description = request.description
+    prompt = PROMPT_TEMPLATE.format(description=description)
+
+    try:
+        # Query Hugging Face model
+        messages = [{"role": "user", "content": prompt}]
+        stream = client.chat.completions.create(
+            model="Qwen/Qwen2.5-Coder-32B-Instruct",
+            messages=messages,
+            temperature=0.5,
+            max_tokens=1024,
+            top_p=0.7,
+            stream=True,
+        )
+
+        # Collect the HTML content from the stream
+        generated_html = ""
+        for chunk in stream:
+            generated_html += chunk.choices[0].delta.content
+
+        # Return the generated HTML content
+        return JSONResponse(content={"html": generated_html})
+
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)