created app.py file

app.py

@@ -0,0 +1,100 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from langchain_huggingface.llms import HuggingFacePipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
+from langchain_core.prompts import ChatPromptTemplate
+
+import os
+import fal_client
+
+# FastAPI app
+app = FastAPI()
+
+# Set the environment variable
+os.environ['FAL_KEY'] = 'bb79b746-999d-4bec-af22-04fddb05d087:49350e8b76fd8dda0fb9dd8442a9ccf5'
+
+# Request body model
+class StoryRequest(BaseModel):
+    mood: str
+    story_type: str
+    theme: str
+    num_scenes: int
+    txt: str
+
+# Initialize the LLM
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+tokenizer = AutoTokenizer.from_pretrained("tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b")
+model = AutoModelForCausalLM.from_pretrained("tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b")
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=2000)
+llm = HuggingFacePipeline(pipeline=pipe)
+
+# Create a prompt template
+# system = """You are a helpful and creative assistant that specializes in generating engaging and imaginative stories for kids.
+# Based on the user's provided mood, preferred story type, theme, age, and desired story length of 500-600 words, create a unique and captivating story.
+# Always start with Story Title then generate a single story and dont ask for any feedback at the end just sign off with a cute closing inviting the reader 
+# to create another adventure soon! 
+# """
+
+system = """You are a helpful and creative assistant that specializes in generating engaging and imaginative short storie for kids.
+Based on the user's provided mood, preferred story type, theme, age, and desired story length of 500-600 words, create a unique and captivating story.
+Always start with Story Title then generate a single story.Storie begin on Page 1(also mention the all pages headings in bold) and end on Page 7.
+Total pages in storie are seven each page have one short paragraph and dont ask for any feedback at the end just sign off with a cute closing inviting the reader 
+to create another adventure soon! 
+"""
+
+prompt_template = ChatPromptTemplate.from_messages([("system", system), ("human", "{text}")])
+
+# FastAPI endpoint to generate the story
+@app.post("/generate_story/")
+async def generate_story(story_request: StoryRequest):
+    story = f"""here are the inputs from user:
+    - **Mood:** {story_request.mood}
+    - **Story Type:** {story_request.story_type}
+    - **Theme:** {story_request.theme}
+    - **Details Provided:** {story_request.txt}
+    """
+    
+    final_prompt = prompt_template.format(text=story)
+
+    # Create the LLMChain
+    # chain = LLMChain(llm=llm, prompt=prompt_template)
+    chain = llm | prompt_template
+    
+    # try:
+    #     response = chain.invoke(final_prompt)
+    #     return {"story": response}
+    # except Exception as e:
+    #     raise HTTPException(status_code=500, detail=str(e))
+    response = chain.invoke(final_prompt)
+    
+    if not response:
+        raise HTTPException(status_code=500, detail="Failed to generate the story")
+    
+    images = []
+    for i in range(story_request.num_scenes):
+        # image_prompt = f"Generate an image for Scene {i+1} based on this story: Mood: {story_request.mood}, Story Type: {story_request.story_type}, Theme: {story_request.theme}. Story: {response}"
+        image_prompt = (
+        f"Generate an image for Scene {i+1}. "
+        f"This image should represent the details described in paragraph {i+1} of the story. "
+        f"Mood: {story_request.mood}, Story Type: {', '.join(story_request.story_type)}, Theme: {story_request.theme}. "
+        f"Story: {response} "
+        f"Focus on the key elements in paragraph {i+1}."
+        )
+        handler = fal_client.submit(
+            "fal-ai/flux/schnell",
+            arguments={
+                "prompt": image_prompt,
+                "num_images": 1,
+                "enable_safety_checker": True
+            },
+        )
+        result = handler.get()
+        image_url = result['images'][0]['url']
+        images.append(image_url)
+    
+    return {
+        "story": response,
+        "images": images
+    }