Update app.py

app.py

@@ -1,287 +1,286 @@
-import yaml
-from together import Together
-from langchain.llms.together import Together as TogetherLLM
-from langchain.prompts import PromptTemplate
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.schema.output_parser import StrOutputParser
-from pinecone import Pinecone
-import gradio as gr
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-
-
-API_FILE_PATH = r"C:\Users\abhay\Analytics Vidhya\API.yml"
-COURSES_FILE_PATH = r"C:\Users\abhay\Analytics Vidhya\courses.json"
-
-def load_api_keys(api_file_path):
-    """Loads API keys from a YAML file."""
-    with open(api_file_path, 'r') as f:
-        api_keys = yaml.safe_load(f)
-    return api_keys
-
-def generate_query_embedding(query, together_api_key):
-    """Generates embedding for the user query."""
-    client = Together(api_key=together_api_key)
-    response = client.embeddings.create(
-        model="WhereIsAI/UAE-Large-V1", input=query
-    )
-    return response.data[0].embedding
-
-def initialize_pinecone(pinecone_api_key):
-    """Initializes Pinecone with API key."""
-    return Pinecone(api_key=pinecone_api_key)
-
-def pinecone_similarity_search(pinecone_instance, index_name, query_embedding, top_k=5):
-    """Performs a similarity search in Pinecone."""
-    try:
-        index = pinecone_instance.Index(index_name)
-        results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
-        if not results.matches:
-            return None
-        return results
-    except Exception as e:
-        print(f"Error during similarity search: {e}")
-        return None
-
-def create_prompt_template():
-    """Creates a prompt template for LLM."""
-    template = """You are a helpful AI course advisor. Based on the following context and query, suggest relevant courses.
-    For each course, explain:
-    1. Why it's relevant to the query
-    2. What the student will learn
-    3. Who should take this course
-    
-    If no relevant courses are found, suggest alternative search terms.
-
-    Context: {context}
-    User Query: {query}
-
-    Response: Let me help you find the perfect courses for your needs! 🎓
-    """
-    return PromptTemplate(template=template, input_variables=["context", "query"])
-
-def initialize_llm(together_api_key):
-    """Initializes Together LLM."""
-    return TogetherLLM(
-        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
-        together_api_key=together_api_key,
-        temperature=0.3,
-        max_tokens=500
-    )
-
-def create_chain(llm, prompt):
-    """Creates a chain using the RunnableSequence approach."""
-    chain = (
-        {"context": RunnablePassthrough(), "query": RunnablePassthrough()}
-        | prompt
-        | llm
-        | StrOutputParser()
-    )
-    return chain
-
-def format_course_info(metadata):
-    """Formats course information with emojis and styling."""
-    return f"""
-📚 **Course Title:** {metadata.get('title', 'No title')}
-
-📝 **Description:** {metadata.get('text', 'No description')}
-
-🔗 **Course Link:** {metadata.get('course_link', 'No link')}
-
-👨‍🏫 **Instructor:** {metadata.get('instructor', 'Not specified')}
-
-⏱️ **Duration:** {metadata.get('duration', 'Not specified')}
-
-📊 **Level:** {metadata.get('difficulty_level', 'Not specified')}
-
-💰 **Price:** {metadata.get('price', 'Not specified')}
-"""
-
-def generate_llm_response(chain, query, retrieved_data):
-    """Generates an LLM response with formatted course information."""
-    try:
-        if not retrieved_data or not retrieved_data.matches:
-            return "🔍 I couldn't find any relevant courses matching your query. Please try different search terms."
-
-        context_parts = []
-        formatted_courses = []
-        
-        for match in retrieved_data.matches:
-            metadata = match.metadata
-            if metadata:
-                context_parts.append(
-                    f"Title: {metadata.get('title', 'No title')}\n"
-                    f"Description: {metadata.get('text', 'No description')}\n"
-                    f"Link: {metadata.get('course_link', 'No link')}"
-                )
-                formatted_courses.append(format_course_info(metadata))
-
-        if not context_parts:
-            return "⚠️ I found some matches but couldn't extract course information. Please try again."
-
-        context = "\n\n".join(context_parts)
-        llm_analysis = chain.invoke({"context": context, "query": query})
-
-        separator = "=" * 50
-        final_response = f"""
-{llm_analysis}
-
-🎯 Here are the detailed course listings:
-{separator}
-{''.join(formatted_courses)}
-"""
-        return final_response
-
-    except Exception as e:
-        print(f"Error generating response: {e}")
-        return "❌ I encountered an error while generating the response. Please try again."
-
-def create_gradio_interface(api_keys):
-    """Creates a custom Gradio interface with improved styling."""
-    # Initialize components
-    pinecone_instance = initialize_pinecone(api_keys["pinecone_api_key"])
-    llm = initialize_llm(api_keys["together_ai_api_key"])
-    prompt = create_prompt_template()
-    chain = create_chain(llm, prompt)
-
-    def process_query(query):
-        try:
-            query_embedding = generate_query_embedding(query, api_keys["together_ai_api_key"])
-            results = pinecone_similarity_search(
-                pinecone_instance, 
-                api_keys["pinecone_index_name"], 
-                query_embedding
-            )
-            response = generate_llm_response(chain, query, results)
-            return response
-        except Exception as e:
-            return f"❌ Error: {str(e)}"
-
-    # Custom CSS for better styling
-    custom_css = """
-    .gradio-container {
-        background-color: #f0f8ff;
-    }
-    .input-box {
-        border: 2px solid #2e86de;
-        border-radius: 10px;
-        padding: 15px;
-        margin: 10px 0;
-    }
-    .output-box {
-        background-color: #ffffff;
-        border: 2px solid #54a0ff;
-        border-radius: 10px;
-        padding: 20px;
-        margin: 10px 0;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-    }
-    .heading {
-        color: #2e86de;
-        text-align: center;
-        margin-bottom: 20px;
-    }
-    .submit-btn {
-        background-color: #2e86de !important;
-        color: white !important;
-        border-radius: 8px !important;
-        padding: 10px 20px !important;
-        font-size: 16px !important;
-    }
-    .examples {
-        margin-top: 20px;
-        padding: 15px;
-        background-color: #f8f9fa;
-        border-radius: 10px;
-    }
-    """
-
-    # Create Gradio interface with custom theme
-    theme = gr.themes.Soft().set(
-        body_background_fill="#f0f8ff",
-        block_background_fill="#ffffff",
-        block_border_width="2px",
-        block_border_color="#2e86de",
-        block_radius="10px",
-        button_primary_background_fill="#2e86de",
-        button_primary_text_color="white",
-        input_background_fill="#ffffff",
-        input_border_color="#2e86de",
-        input_radius="8px",
-    )
-
-    with gr.Blocks(theme=theme, css=custom_css) as demo:
-        gr.Markdown(
-            """
-            # 🎓 Course Recommendation Assistant
-            
-            Welcome to your personalized course finder! Ask me about any topics you're interested in learning.
-            I'll help you discover the perfect courses from Analytics Vidhya's collection.
-            
-            ## 🌟 Features:
-            - 📚 Detailed course recommendations
-            - 🎯 Learning path suggestions
-            - 📊 Course difficulty levels
-            - 💰 Price information
-            """,
-            elem_classes=["heading"]
-        )
-        
-        with gr.Row():
-            with gr.Column():
-                query_input = gr.Textbox(
-                    label="What would you like to learn? 🤔",
-                    placeholder="e.g., 'machine learning for beginners' or 'advanced python courses'",
-                    lines=3,
-                    elem_classes=["input-box"]
-                )
-                submit_btn = gr.Button(
-                    "🔍 Find Courses",
-                    variant="primary",
-                    elem_classes=["submit-btn"]
-                )
-
-        with gr.Row():
-            output = gr.Markdown(
-                label="Recommendations 📚",
-                elem_classes=["output-box"]
-            )
-
-        with gr.Row(elem_classes=["examples"]):
-            gr.Examples(
-                examples=[
-                    ["I want to learn machine learning from scratch"],
-                    ["Advanced deep learning courses"],
-                    ["Data visualization tutorials"],
-                    ["Python programming for beginners"],
-                    ["Natural Language Processing courses"]
-                ],
-                inputs=query_input,
-                label="📝 Example Queries"
-            )
-
-        submit_btn.click(
-            fn=process_query,
-            inputs=query_input,
-            outputs=output
-        )
-
-    return demo
-
-def main():
-    try:
-        
-        api_keys = load_api_keys(API_FILE_PATH)
-        
-        
-        demo = create_gradio_interface(api_keys)
-        demo.launch(
-            share=True)
-
-    except Exception as e:
-        print(f"An error occurred during initialization: {str(e)}")
-
-if __name__ == "__main__":
-    main()
+import yaml
+from together import Together
+from langchain.prompts import PromptTemplate
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from pinecone import Pinecone
+import gradio as gr
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+
+API_FILE_PATH = r"C:\Users\abhay\Analytics Vidhya\API.yml"
+COURSES_FILE_PATH = r"C:\Users\abhay\Analytics Vidhya\courses.json"
+
+def load_api_keys(api_file_path):
+    """Loads API keys from a YAML file."""
+    with open(api_file_path, 'r') as f:
+        api_keys = yaml.safe_load(f)
+    return api_keys
+
+def generate_query_embedding(query, together_api_key):
+    """Generates embedding for the user query."""
+    client = Together(api_key=together_api_key)
+    response = client.embeddings.create(
+        model="WhereIsAI/UAE-Large-V1", input=query
+    )
+    return response.data[0].embedding
+
+def initialize_pinecone(pinecone_api_key):
+    """Initializes Pinecone with API key."""
+    return Pinecone(api_key=pinecone_api_key)
+
+def pinecone_similarity_search(pinecone_instance, index_name, query_embedding, top_k=5):
+    """Performs a similarity search in Pinecone."""
+    try:
+        index = pinecone_instance.Index(index_name)
+        results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
+        if not results.matches:
+            return None
+        return results
+    except Exception as e:
+        print(f"Error during similarity search: {e}")
+        return None
+
+def create_prompt_template():
+    """Creates a prompt template for LLM."""
+    template = """You are a helpful AI course advisor. Based on the following context and query, suggest relevant courses.
+    For each course, explain:
+    1. Why it's relevant to the query
+    2. What the student will learn
+    3. Who should take this course
+    
+    If no relevant courses are found, suggest alternative search terms.
+
+    Context: {context}
+    User Query: {query}
+
+    Response: Let me help you find the perfect courses for your needs! 🎓
+    """
+    return PromptTemplate(template=template, input_variables=["context", "query"])
+
+def initialize_llm(together_api_key):
+    """Initializes Together LLM."""
+    return TogetherLLM(
+        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        together_api_key=together_api_key,
+        temperature=0.3,
+        max_tokens=500
+    )
+
+def create_chain(llm, prompt):
+    """Creates a chain using the RunnableSequence approach."""
+    chain = (
+        {"context": RunnablePassthrough(), "query": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    return chain
+
+def format_course_info(metadata):
+    """Formats course information with emojis and styling."""
+    return f"""
+📚 **Course Title:** {metadata.get('title', 'No title')}
+
+📝 **Description:** {metadata.get('text', 'No description')}
+
+🔗 **Course Link:** {metadata.get('course_link', 'No link')}
+
+👨‍🏫 **Instructor:** {metadata.get('instructor', 'Not specified')}
+
+⏱️ **Duration:** {metadata.get('duration', 'Not specified')}
+
+📊 **Level:** {metadata.get('difficulty_level', 'Not specified')}
+
+💰 **Price:** {metadata.get('price', 'Not specified')}
+"""
+
+def generate_llm_response(chain, query, retrieved_data):
+    """Generates an LLM response with formatted course information."""
+    try:
+        if not retrieved_data or not retrieved_data.matches:
+            return "🔍 I couldn't find any relevant courses matching your query. Please try different search terms."
+
+        context_parts = []
+        formatted_courses = []
+        
+        for match in retrieved_data.matches:
+            metadata = match.metadata
+            if metadata:
+                context_parts.append(
+                    f"Title: {metadata.get('title', 'No title')}\n"
+                    f"Description: {metadata.get('text', 'No description')}\n"
+                    f"Link: {metadata.get('course_link', 'No link')}"
+                )
+                formatted_courses.append(format_course_info(metadata))
+
+        if not context_parts:
+            return "⚠️ I found some matches but couldn't extract course information. Please try again."
+
+        context = "\n\n".join(context_parts)
+        llm_analysis = chain.invoke({"context": context, "query": query})
+
+        separator = "=" * 50
+        final_response = f"""
+{llm_analysis}
+
+🎯 Here are the detailed course listings:
+{separator}
+{''.join(formatted_courses)}
+"""
+        return final_response
+
+    except Exception as e:
+        print(f"Error generating response: {e}")
+        return "❌ I encountered an error while generating the response. Please try again."
+
+def create_gradio_interface(api_keys):
+    """Creates a custom Gradio interface with improved styling."""
+    # Initialize components
+    pinecone_instance = initialize_pinecone(api_keys["pinecone_api_key"])
+    llm = initialize_llm(api_keys["together_ai_api_key"])
+    prompt = create_prompt_template()
+    chain = create_chain(llm, prompt)
+
+    def process_query(query):
+        try:
+            query_embedding = generate_query_embedding(query, api_keys["together_ai_api_key"])
+            results = pinecone_similarity_search(
+                pinecone_instance, 
+                api_keys["pinecone_index_name"], 
+                query_embedding
+            )
+            response = generate_llm_response(chain, query, results)
+            return response
+        except Exception as e:
+            return f"❌ Error: {str(e)}"
+
+    # Custom CSS for better styling
+    custom_css = """
+    .gradio-container {
+        background-color: #f0f8ff;
+    }
+    .input-box {
+        border: 2px solid #2e86de;
+        border-radius: 10px;
+        padding: 15px;
+        margin: 10px 0;
+    }
+    .output-box {
+        background-color: #ffffff;
+        border: 2px solid #54a0ff;
+        border-radius: 10px;
+        padding: 20px;
+        margin: 10px 0;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    .heading {
+        color: #2e86de;
+        text-align: center;
+        margin-bottom: 20px;
+    }
+    .submit-btn {
+        background-color: #2e86de !important;
+        color: white !important;
+        border-radius: 8px !important;
+        padding: 10px 20px !important;
+        font-size: 16px !important;
+    }
+    .examples {
+        margin-top: 20px;
+        padding: 15px;
+        background-color: #f8f9fa;
+        border-radius: 10px;
+    }
+    """
+
+    # Create Gradio interface with custom theme
+    theme = gr.themes.Soft().set(
+        body_background_fill="#f0f8ff",
+        block_background_fill="#ffffff",
+        block_border_width="2px",
+        block_border_color="#2e86de",
+        block_radius="10px",
+        button_primary_background_fill="#2e86de",
+        button_primary_text_color="white",
+        input_background_fill="#ffffff",
+        input_border_color="#2e86de",
+        input_radius="8px",
+    )
+
+    with gr.Blocks(theme=theme, css=custom_css) as demo:
+        gr.Markdown(
+            """
+            # 🎓 Course Recommendation Assistant
+            
+            Welcome to your personalized course finder! Ask me about any topics you're interested in learning.
+            I'll help you discover the perfect courses from Analytics Vidhya's collection.
+            
+            ## 🌟 Features:
+            - 📚 Detailed course recommendations
+            - 🎯 Learning path suggestions
+            - 📊 Course difficulty levels
+            - 💰 Price information
+            """,
+            elem_classes=["heading"]
+        )
+        
+        with gr.Row():
+            with gr.Column():
+                query_input = gr.Textbox(
+                    label="What would you like to learn? 🤔",
+                    placeholder="e.g., 'machine learning for beginners' or 'advanced python courses'",
+                    lines=3,
+                    elem_classes=["input-box"]
+                )
+                submit_btn = gr.Button(
+                    "🔍 Find Courses",
+                    variant="primary",
+                    elem_classes=["submit-btn"]
+                )
+
+        with gr.Row():
+            output = gr.Markdown(
+                label="Recommendations 📚",
+                elem_classes=["output-box"]
+            )
+
+        with gr.Row(elem_classes=["examples"]):
+            gr.Examples(
+                examples=[
+                    ["I want to learn machine learning from scratch"],
+                    ["Advanced deep learning courses"],
+                    ["Data visualization tutorials"],
+                    ["Python programming for beginners"],
+                    ["Natural Language Processing courses"]
+                ],
+                inputs=query_input,
+                label="📝 Example Queries"
+            )
+
+        submit_btn.click(
+            fn=process_query,
+            inputs=query_input,
+            outputs=output
+        )
+
+    return demo
+
+def main():
+    try:
+        
+        api_keys = load_api_keys(API_FILE_PATH)
+        
+        
+        demo = create_gradio_interface(api_keys)
+        demo.launch(
+            share=True)
+
+    except Exception as e:
+        print(f"An error occurred during initialization: {str(e)}")
+
+if __name__ == "__main__":
+    main()